Richard K. G. Do

Computer Vision and Pattern Recognition · with Kim · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 106 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 36 citations into it
• Artificial Intelligence — 33 citations into it
• Model Architectures — 15 citations into it
• Optimization & Training — 12 citations into it
• Safety, Robustness & Interpretability — 6 citations into it
• Biophysics — 5 citations into it
• Media Technology — 5 citations into it

Whom Richard K. G. Do trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Karen Simonyan
5. Christian Szegedy
6. Ilya Sutskever
7. Yoshua Bengio
8. Shaoqing Ren
9. Diederik P. Kingma
10. Yann LeCun

Papers from Richard K. G. Do's perspective

Ordered by how strongly the researchers Richard K. G. Do trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet Large Scale Visual Recognition Challenge
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• The Medical Segmentation Decathlon (2021) — 1,513 citations
• Convolutional neural networks: an overview and application in radiology (2018) — 4,045 citations